Solving the eigendecomposition of the covariance matrix \(C\) , we can derive the eigenvectors corresponding to the principal eigenvalues, that define an orthogonal frame at point \(\mathbf {p}_i\) . The eigenvalues \(\lambda _i\)  measure the variation along the axis defined by their corresponding eigenvector. Intuitively, the eigenvectors that correspond to the largest eigenvalues span the tangent plane at point \(\mathbf {p}_i\) , whereas the eigenvector corresponding to the smallest eigenvalue can be used to approximate the surface normal \(n_i\) . Thus, given that the smallest eigenvalue measures the deviation of point \(\mathbf {p}_i\)  from the surface, it can be used as an estimate of point curvature. As shown in [1]}, we may define:
\(\kappa (\mathbf {p}_i) = \frac{\lambda _0}{\lambda _0+\lambda _1+\lambda _2}, \quad \lambda _0<\lambda _1<\lambda _2\) 
